OpenRaiser/Intern-Atlas huggingface dataset:
Who "Controls" Where Work Shall be Done? State-of-Practice in Post-Pandemic Remote Work Regulation
Venue: arXiv cs.SE 2025
Year: 2025

The COVID-19 pandemic has permanently altered workplace structures, making remote work a widespread practice. While many employees advocate for flexibility, many employers reconsider their attitude toward remote work and opt for structured return-to-office mandates. Media headlines repeatedly emphasize that the corporate world is returning to full-time office work. This study examines how companies employing software engineers and supporting roles regulate work location, whether corporate policies have evolved in the last five years, and, if so, how, and why. We collected data on remote work regulation from corporate HR and/or management representatives from 68 corporate entities that vary in size, location, and orientation towards remote or office work. Our findings reveal that although many companies prioritize office-centred working (50%), most companies in our sample permit hybrid working to varying degrees (85%). Remote work regulation does not reveal any particular new "best practice" as policies differ greatly, but the single most popular arrangement was the three in-office days per week. More than half of the companies (51%) encourage or mandate office days, and more than quarter (28%) have changed regulations, gradually increasing the mandatory office presence or implementing differentiated conditions. Although no companies have increased flexibility, only four companies are returning to full-time office work. Our key recommendation for office-oriented companies is to consider a trust-based alternative to strict office presence mandates, while for companies oriented toward remote working, we warn about the points of no (or hard) return. Finally, the current state of policies is clearly not final, as companies continue to experiment and adjust their work regulation.
State Characterisation of Self-Directed Channel Memristive Devices
Venue: arXiv cs.ET 2025
Year: 2025

Knowing how to reliably use memristors as information storage devices is crucial not only to their role as emerging memories, but also for their application in neural network acceleration and as components of novel neuromorphic systems. In order to better understand the dynamics of information storage on memristors, it is essential to be able to characterise and measure their state. To this end, in this paper we propose a general, physics-inspired modelling approach for characterising the state of self-directed channel (SDC) memristors. Additionally, to enable the identification of the proposed state from device data, we introduce a noise-aware approach to the minimum-variance estimation of the state from voltage and current pairs.
MIKU-PAL: An Automated and Standardized Multi-Modal Method for Speech Paralinguistic and Affect Labeling
Venue: arXiv cs.SD 2025
Year: 2025

Acquiring large-scale emotional speech data with strong consistency remains a challenge for speech synthesis. This paper presents MIKU-PAL, a fully automated multimodal pipeline for extracting high-consistency emotional speech from unlabeled video data. Leveraging face detection and tracking algorithms, we developed an automatic emotion analysis system using a multimodal large language model (MLLM). Our results demonstrate that MIKU-PAL can achieve human-level accuracy (68.5% on MELD) and superior consistency (0.93 Fleiss kappa score) while being much cheaper and faster than human annotation. With the high-quality, flexible, and consistent annotation from MIKU-PAL, we can annotate fine-grained speech emotion categories of up to 26 types, validated by human annotators with 83% rationality ratings. Based on our proposed system, we further released a fine-grained emotional speech dataset MIKU-EmoBench(131.2 hours) as a new benchmark for emotional text-to-speech and visual voice cloning.
Neural Conditional Transport Maps
Venue: arXiv cs.LG 2025
Year: 2025

We present a neural framework for learning conditional optimal transport (OT) maps between probability distributions. Our approach introduces a conditioning mechanism capable of processing both categorical and continuous conditioning variables simultaneously. At the core of our method lies a hypernetwork that generates transport layer parameters based on these inputs, creating adaptive mappings that outperform simpler conditioning methods. Comprehensive ablation studies demonstrate the superior performance of our method over baseline configurations. Furthermore, we showcase an application to global sensitivity analysis, offering high performance in computing OT-based sensitivity indices. This work advances the state-of-the-art in conditional optimal transport, enabling broader application of optimal transport principles to complex, high-dimensional domains such as generative modeling and black-box model explainability.
Learning to Reason via Mixture-of-Thought for Logical Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Human beings naturally utilize multiple reasoning modalities to learn and solve logical problems, i.e., different representational formats such as natural language, code, and symbolic logic. In contrast, most existing LLM-based approaches operate with a single reasoning modality during training, typically natural language. Although some methods explored modality selection or augmentation at inference time, the training process remains modality-blind, limiting synergy among modalities. To fill in this gap, we propose Mixture-of-Thought (MoT), a framework that enables LLMs to reason across three complementary modalities: natural language, code, and a newly introduced symbolic modality, truth-table, which systematically enumerates logical cases and partially mitigates key failure modes in natural language reasoning. MoT adopts a two-phase design: (1) self-evolving MoT training, which jointly learns from filtered, self-generated rationales across modalities; and (2) MoT inference, which fully leverages the synergy of three modalities to produce better predictions. Experiments on logical reasoning benchmarks including FOLIO and ProofWriter demonstrate that our MoT framework consistently and significantly outperforms strong LLM baselines with single-modality chain-of-thought approaches, achieving up to +11.7pp average accuracy gain. Further analyses show that our MoT framework benefits both training and inference stages; that it is particularly effective on harder logical reasoning problems; and that different modalities contribute complementary strengths, with truth-table reasoning helping to overcome key bottlenecks in natural language inference.
HQSI: Hybrid Quantum Swarm Intelligence -- A Case Study of Online Certificate Status Protocol Request Flow Prediction
Venue: arXiv cs.NE 2025
Year: 2025

As quantum computing technology continues to advance, various sectors, including industry, government, academia, and research, have increasingly focused on its future applications. With the integration of artificial intelligence techniques, multiple Quantum Neural Network (QNN) models have been proposed, including quantum convolutional neural networks, quantum long short-term memory networks, and quantum generative adversarial networks. Furthermore, optimization methods such as constrained optimization by linear approximation and simultaneous perturbation stochastic approximation have been explored. Therefore, this study proposes Hybrid Quantum Swarm Intelligence (HQSI), which constructs a QNN model as a forward propagation neural network. After measuring quantum states and obtaining prediction results, a classical computer-based swarm intelligence algorithm is employed for weight optimization. The training process iterates between quantum and classical computing environments. During the experimental phase, the proposed HQSI method is evaluated using an online certificate status protocol request traffic prediction task. Comparative analysis against state-of-the-art quantum optimization algorithms demonstrates that the proposed HQSI approach achieves more than a 50% reduction in error.
From Hand-Crafted Metrics to Evolved Training-Free Performance Predictors for Neural Architecture Search via Genetic Programming
Venue: arXiv cs.NE 2025
Year: 2025

Estimating the network performance using zero-cost (ZC) metrics has proven both its efficiency and efficacy in Neural Architecture Search (NAS). However, a notable limitation of most ZC proxies is their inconsistency, as reflected by the substantial variation in their performance across different problems. Furthermore, the design of existing ZC metrics is manual, involving a time-consuming trial-and-error process that requires substantial domain expertise. These challenges raise two critical questions: (1) Can we automate the design of ZC metrics? and (2) Can we utilize the existing hand-crafted ZC metrics to synthesize a more generalizable one? In this study, we propose a framework based on Symbolic Regression via Genetic Programming to automate the design of ZC metrics. Our framework is not only highly extensible but also capable of quickly producing a ZC metric with a strong positive rank correlation to true network performance across diverse NAS search spaces and tasks. Extensive experiments on 13 problems from NAS-Bench-Suite-Zero demonstrate that our automatically generated proxies consistently outperform hand-crafted alternatives. Using our evolved proxy metric as the search objective in an evolutionary algorithm, we could identify network architectures with competitive performance within 15 minutes using a single consumer GPU.
Web2Wiki: Characterizing Wikipedia Linking Across the Web
Venue: arXiv cs.SI 2025
Year: 2025

Wikipedia is one of the most visited websites globally, yet its role beyond its own platform remains largely unexplored. In this paper, we present the first large-scale analysis of how Wikipedia is referenced across the Web. Using a dataset from Common Crawl, we identify over 90 million Wikipedia links spanning 1.68% of Web domains and examine their distribution, context, and function. Our analysis of English Wikipedia reveals three key findings: (1) Wikipedia is most frequently cited by news and science websites for informational purposes, while commercial websites reference it less often. (2) The majority of Wikipedia links appear within the main content rather than in boilerplate or user-generated sections, highlighting their role in structured knowledge presentation. (3) Most links (95%) serve as explanatory references rather than as evidence or attribution, reinforcing Wikipedia's function as a background knowledge provider. While this study focuses on English Wikipedia, our publicly released Web2Wiki dataset includes links from multiple language editions, supporting future research on Wikipedia's global influence on the Web.
Curriculum Learning in Genetic Programming Guided Local Search for Large-scale Vehicle Routing Problems
Venue: arXiv cs.NE 2025
Year: 2025

Manually designing (meta-)heuristics for the Vehicle Routing Problem (VRP) is a challenging task that requires significant domain expertise. Recently, data-driven approaches have emerged as a promising solution, automatically learning heuristics that perform well on training instances and generalize to unseen test cases. Such an approach learns (meta-)heuristics that can perform well on the training instances, expecting it to generalize well on the unseen test instances. A recent method, named GPGLS, uses Genetic Programming (GP) to learn the utility function in Guided Local Search (GLS) and solved large scale VRP effectively. However, the selection of appropriate training instances during the learning process remains an open question, with most existing studies including GPGLS relying on random instance selection. To address this, we propose a novel method, CL-GPGLS, which integrates Curriculum Learning (CL) into GPGLS. Our approach leverages a predefined curriculum to introduce training instances progressively, starting with simpler tasks and gradually increasing complexity, enabling the model to better adapt and optimize for large-scale VRP (LSVRP). Extensive experiments verify the effectiveness of CL-GPGLS, demonstrating significant performance improvements over three baseline methods.
Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models
Venue: arXiv cs.LG 2025
Year: 2025

Graph Transformers, leveraging the global attention to capture long-range dependencies in graph structures, have significantly advanced graph machine learning, but face prohibitive computational complexity. Tokenized Graph Learning Models (TGLMs) address this issue by converting graphs into ordered token lists for scalable processing. Besides, TGLMs also empower Large Language Models (LLMs) to handle text-attributed graphs more effectively and thus are also employed in Graph LLMs. However, existing TGLMs rely on hand-designed token lists and their adaptability to diverse graph learning scenarios remains unexplored. In this paper, we first conduct extensive empirical and theoretical preliminary studies for hand-designed token lists. Surprisingly, we identify an unexplored hop-overpriority problem: the common pre-defined token lists overemphasize nearby nodes and overwhelm the ability of TGLMs to balance local and global signals. This phenomenon is especially harmful for heterophilic graphs. To address this problem, we propose the Learnable Graph Token List (LGTL), a plug-and-play module to replace hand-designed token lists in TGLMs. Specifically, LGTL adaptively adjusts the weights across hops and prioritizes informative nodes within hops through a graph attention gate module and a selection module, respectively. In this way, contextually informative nodes can be adaptively emphasized for both homophilic and heterophilic graphs. Besides, we theoretically show that LGTL can address the hop-overpriority problem. Extensive experiments on benchmarks validate the efficacy of LGTL across both Graph Transformers and Graph LLM backbones.
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation
Venue: arXiv cs.IR 2025
Year: 2025

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding responses with retrieved information. As an emerging paradigm, Agentic RAG further enhances this process by introducing autonomous LLM agents into the information seeking process. However, existing benchmarks fall short in evaluating such systems, as they are confined to a static retrieval environment with a fixed, limited corpus} and simple queries that fail to elicit agentic behavior. Moreover, their evaluation protocols assess information seeking effectiveness by pre-defined gold sets of documents, making them unsuitable for the open-ended and dynamic nature of real-world web environments. To bridge this gap, we present InfoDeepSeek, a new benchmark with challenging questions designed for assessing agentic information seeking in real-world, dynamic web environments. We propose a systematic methodology for constructing challenging queries satisfying the criteria of determinacy, difficulty, and diversity. Based on this, we develop the first evaluation framework tailored to dynamic agentic information seeking, including fine-grained metrics about the accuracy, utility, and compactness of information seeking outcomes. Through extensive experiments across LLMs, search engines, and question types, InfoDeepSeek reveals nuanced agent behaviors and offers actionable insights for future research.
Capacitated Fair-Range Clustering: Hardness and Approximation Algorithms
Venue: arXiv cs.DS 2025
Year: 2025

Capacitated fair-range $k$-clustering generalizes classical $k$-clustering by incorporating both capacity constraints and demographic fairness. In this setting, each facility has a capacity limit and may belong to one or more demographic groups. The task is to select $k$ facilities as centers and assign each client to a center such that: ($a$) no center exceeds its capacity, ($b$) the number of centers selected from each group lies within specified lower and upper bounds (fair-range constraints), and ($c$) the clustering cost (e.g., $k$-median or $k$-means) is minimized. Prior work by Thejaswi et al. (KDD 2022) showed that satisfying fair-range constraints is NP-hard, making the problem inapproximable to any polynomial factor. We strengthen this result by showing that inapproximability persists even when the fair-range constraints are trivially satisfiable, highlighting the intrinsic computational complexity of the clustering task itself. Assuming standard complexity conjectures, we show that no non-trivial approximation is possible without exhaustively enumerating all $k$-subsets of the facility set. Notably, our inapproximability results hold even on tree metrics and when the number of groups is logarithmic in the size of the facility set. In light of these strong inapproximability results, we focus on a more practical setting where the number of groups is constant. In this regime, we design two approximation algorithms: ($i$) a polynomial-time $O(\log k)$- and $O(\log^2 k)$-approximation algorithm for the $k$-median and $k$-means objectives, and ($ii$) a fixed-parameter tractable algorithm parameterized by $k$, achieving $(3+ε)$- and $(9 + ε)$-approximation, respectively. These results match the best-known approximation guarantees for capacitated clustering without fair-range constraints and resolves an open question posed by Zang et al. (NeurIPS 2024).
VERDI: VLM-Embedded Reasoning for Autonomous Driving
Venue: arXiv cs.RO 2025
Year: 2025

While autonomous driving (AD) stacks struggle with decision making under partial observability and real-world complexity, human drivers are capable of commonsense reasoning to make near-optimal decisions with limited information. Recent work has attempted to leverage finetuned Vision-Language Models (VLMs) for trajectory planning at inference time to emulate human behavior. Despite their success in benchmark evaluations, these methods are often impractical to deploy (a 70B parameter VLM inference at merely 8 tokens per second requires more than 160G of memory), and their monolithic network structure prohibits safety decomposition. To bridge this gap, we propose VLM-Embedded Reasoning for autonomous Driving (VERDI), a training-time framework that distills the reasoning process and commonsense knowledge of VLMs into the AD stack. VERDI augments modular differentiable end-to-end (e2e) AD models by aligning intermediate module outputs at the perception, prediction, and planning stages with text features explaining the driving reasoning process produced by VLMs. By encouraging alignment in latent space, VERDI enables the modular AD stack to internalize structured reasoning, without incurring the inference-time costs of large VLMs. We validate VERDI in both open-loop (NuScenes and Bench2Drive benchmarks) and closed-loop (HugSim Simulator) settings. We find that VERDI outperforms existing e2e methods that do not embed reasoning by up to 11% in $\ell_{2}$ distance and 11% in driving performance, while maintaining real-time inference speed.
Self-heating electrochemical memory for high-precision analog computing
Venue: arXiv cs.ET 2025
Year: 2025

Analog computers hold promise to significantly reduce the energy consumption of artificial intelligence algorithms, but commercialization has been hampered by a fundamental scientific challenge - how to reliably store and process analog information with high precision. We present an approach based upon metal oxide memory cells that undergo controlled self-heating during programming with a newly developed, electro-thermo-chemical gate. The gate uniformly spreads heat and electrochemical reactions to enable wide, bulk-vacancy modulation which yields nine orders of magnitude in tunable analog resistance - three orders greater than other devices reported, with thousands of states. The gating profoundly reduces noise and drift to enable precision programming to targeted states within a few operations, lowering conductance errors by two orders of magnitude relative to other devices reported. Simulations show improvement in computational energy efficiency by at least 10x over other devices due to far greater scalability at higher precision. The results overturn long-held assumptions about the poor reliability and precision of analog resistance devices and opens the door to manufacturable, bulk metal-oxide devices and new applications that leverage high precision.
VideoGameQA-Bench: Evaluating Vision-Language Models for Video Game Quality Assurance
Venue: arXiv cs.CV 2025
Year: 2025

With video games now generating the highest revenues in the entertainment industry, optimizing game development workflows has become essential for the sector's sustained growth. Recent advancements in Vision-Language Models (VLMs) offer considerable potential to automate and enhance various aspects of game development, particularly Quality Assurance (QA), which remains one of the industry's most labor-intensive processes with limited automation options. To accurately evaluate the performance of VLMs in video game QA tasks and determine their effectiveness in handling real-world scenarios, there is a clear need for standardized benchmarks, as existing benchmarks are insufficient to address the specific requirements of this domain. To bridge this gap, we introduce VideoGameQA-Bench, a comprehensive benchmark that covers a wide array of game QA activities, including visual unit testing, visual regression testing, needle-in-a-haystack tasks, glitch detection, and bug report generation for both images and videos of various games. Code and data are available at: https://asgaardlab.github.io/videogameqa-bench/
Modular Fault-Tolerant DBMS
Venue: arXiv cs.DB 2025
Year: 2025

The article addresses the problem of storing data in extreme environmental conditions with limited computing resources and memory. There is a requirement to create portable, fault-tolerant, modular database management systems (DBMS) that are optimized for use in embedded systems. Existing databases, such as LittleDB, LMDB, and Berkeley DB, are reviewed, and their limitations are identified. A variant of a portable DBMS is introduced to efficiently manage data in environments where computational resource usage must be minimized, while meeting specific requirements for fault tolerance and noise immunity. Common solutions for optimizing of insertion, storage and management of data are reviewed. Algorithms for fault-tolerant data encoding in RAM are implemented. An architectural solution to data storage and minimizing the impact of bit errors is proposed. Software that manages relational data in extreme conditions is developed, that allows testing and comparing results with existing solutions.
Integrating Robotic Navigation with Blockchain: A Novel PoS-Based Approach for Heterogeneous Robotic Teams
Venue: arXiv cs.RO 2025
Year: 2025

This work explores a novel integration of blockchain methodologies with Wide Area Visual Navigation (WAVN) to address challenges in visual navigation for a heterogeneous team of mobile robots deployed for unstructured applications in agriculture, forestry, etc. Focusing on overcoming challenges such as GPS independence, environmental changes, and computational limitations, the study introduces the Proof of Stake (PoS) mechanism, commonly used in blockchain systems, into the WAVN framework \cite{Lyons_2022}. This integration aims to enhance the cooperative navigation capabilities of robotic teams by prioritizing robot contributions based on their navigation reliability. The methodology involves a stake weight function, consensus score with PoS, and a navigability function, addressing the computational complexities of robotic cooperation and data validation. This innovative approach promises to optimize robotic teamwork by leveraging blockchain principles, offering insights into the scalability, efficiency, and overall system performance. The project anticipates significant advancements in autonomous navigation and the broader application of blockchain technology beyond its traditional financial context.